In [3]:
# Load calibration data
indi2stitched_mapping = {}
for cam_id in [0, 2, 4, 6, 8]:
# the mapping here has shape (480, 752, 10), where 10 is the x and y coordinates of up to 5 matched points on stitched image
# if there is 1 matched point, the first 2 values are the coordinates of the matched point, and the rest are -1
# if there are 2 matched points, the first 4 values are the coordinates of the matched points, and the rest are -1
# and so on
indi2stitched_mapping[cam_id] = np.load(
join(calibration_dir, "indi2stitch_mappings", f"indi2stitch_mapping_camera_{cam_id}.npy"))
stitched_image = np.zeros((480, 3760, 3), dtype=np.uint8)
# generate 5 images with 5 colors
colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
for i, cam_id in enumerate([6, 8, 0, 2, 4]):
img = np.zeros((480, 752, 3), dtype=np.uint8)
img[:, :, :] = colors[i]
yI, xI = np.indices((480, 752)) # get the y and x coordinates of the individual image
yxS = indi2stitched_mapping[cam_id][yI, xI, :] # get the matched points on the stitched image
yxS = yxS[yxS != -1] # remove the -1 values
yS, xS = yxS[::2].astype(np.int32), yxS[1::2].astype(np.int32) # get the y and x coordinates of the matched points
stitched_image[yS, xS] = colors[
i] # fill the matched points on the stitched image with the color of the individual image
# visualize the stitched image
plt.imshow(stitched_image) # Here we can see the 5 images are stitched together
# cv2.imshow("stitched_image", stitched_image)
# if cv2.waitKey(0) & 0xFF == ord('q'):
# cv2.destroyAllWindows()
Out[3]:
<matplotlib.image.AxesImage at 0x7ea2e8b76310>
